Admit it: Your big data is just a big waste of money and server storage space.

No matter how much of it you collect, no matter how many analytics programs you run, no matter how many data scientists you have pouring over the bits and bytes -- big data is not boosting the bottom line or making your business smarter.

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You’re not the only one facing this problem, according to recent articles in the Harvard Business Review and McKinsey. But don’t blame big data for that. It’s all your fault.

You’ve been focused on analyzing the data rather than extracting intelligence from it and taking quick action on that information. Big data would be more than happy to help you achieve these goals, but only if you use it correctly.

It's possible to transform your big data into an amazingly accurate soothsayer using process-centric and process-embedded AI and machine learning. In order to do so, you have to overcome the three biggest challenges in the quest to monetize big data: complexity, consumerization and continuity.

I'll dig deeper into each of the three challenges in future articles, but first, here's an overview of the aforementioned challenges.

Challenge No. 1: Confronting Complexity

A recent article in Forbes reported that data scientists devote up to 80% of their time collecting and massaging data before even trying to extract anything useful from it. Some even termed these tasks as “data janitor work” and “data wrangling.”